154 related articles for article (PubMed ID: 34941924)
1. Supervised and unsupervised learning to classify scoliosis and healthy subjects based on non-invasive rasterstereography analysis.
Colombo T; Mangone M; Agostini F; Bernetti A; Paoloni M; Santilli V; Palagi L
PLoS One; 2021; 16(12):e0261511. PubMed ID: 34941924
[TBL] [Abstract][Full Text] [Related]
2. Raster stereography versus radiography in the long-term follow-up of idiopathic scoliosis.
Schulte TL; Hierholzer E; Boerke A; Lerner T; Liljenqvist U; Bullmann V; Hackenberg L
J Spinal Disord Tech; 2008 Feb; 21(1):23-8. PubMed ID: 18418132
[TBL] [Abstract][Full Text] [Related]
3. Accuracy of rasterstereography versus radiography in idiopathic scoliosis after anterior correction and fusion.
Hackenberg L; Hierholzer E; Liljenqvist U
Stud Health Technol Inform; 2002; 91():241-5. PubMed ID: 15457730
[TBL] [Abstract][Full Text] [Related]
4. Non invasive classification system of scoliosis curve types using least-squares support vector machines.
Adankon MM; Dansereau J; Labelle H; Cheriet F
Artif Intell Med; 2012 Oct; 56(2):99-107. PubMed ID: 23017984
[TBL] [Abstract][Full Text] [Related]
5. [3-dimensional surface measurement of spinal deformities with video rasterstereography].
Liljenqvist U; Halm H; Hierholzer E; Drerup B; Weiland M
Z Orthop Ihre Grenzgeb; 1998; 136(1):57-64. PubMed ID: 9563188
[TBL] [Abstract][Full Text] [Related]
6. Rasterstereographic back shape analysis in idiopathic scoliosis after anterior correction and fusion.
Hackenberg L; Hierholzer E; Pötzl W; Götze C; Liljenqvist U
Clin Biomech (Bristol, Avon); 2003 Jan; 18(1):1-8. PubMed ID: 12527240
[TBL] [Abstract][Full Text] [Related]
7. [Scanning stereographic surface measurement in idiopathic scoliosis after VDS (ventral derotation spondylodesis)].
Hackenberg L; Liljenqvist U; Hierholzer E; Halm H
Z Orthop Ihre Grenzgeb; 2000; 138(4):353-9. PubMed ID: 11033906
[TBL] [Abstract][Full Text] [Related]
8. Three-dimensional morphology study of surgical adolescent idiopathic scoliosis patient from encoded geometric models.
Thong W; Parent S; Wu J; Aubin CE; Labelle H; Kadoury S
Eur Spine J; 2016 Oct; 25(10):3104-3113. PubMed ID: 26851954
[TBL] [Abstract][Full Text] [Related]
9. Single-slice Alzheimer's disease classification and disease regional analysis with Supervised Switching Autoencoders.
Mendoza-Léon R; Puentes J; Uriza LF; Hernández Hoyos M
Comput Biol Med; 2020 Jan; 116():103527. PubMed ID: 31765915
[TBL] [Abstract][Full Text] [Related]
10. Unsupervised and self-supervised deep learning approaches for biomedical text mining.
Nadif M; Role F
Brief Bioinform; 2021 Mar; 22(2):1592-1603. PubMed ID: 33569575
[TBL] [Abstract][Full Text] [Related]
11. Clinical validation of coronal and sagittal spinal curve measurements based on three-dimensional vertebra vector parameters.
Somoskeöy S; Tunyogi-Csapó M; Bogyó C; Illés T
Spine J; 2012 Oct; 12(10):960-8. PubMed ID: 23018164
[TBL] [Abstract][Full Text] [Related]
12. Deep Learning Model to Classify and Monitor Idiopathic Scoliosis in Adolescents Using a Single Smartphone Photograph.
Zhang T; Zhu C; Zhao Y; Zhao M; Wang Z; Song R; Meng N; Sial A; Diwan A; Liu J; Cheung JPY
JAMA Netw Open; 2023 Aug; 6(8):e2330617. PubMed ID: 37610748
[TBL] [Abstract][Full Text] [Related]
13. Video raster stereography back shape reconstruction: a reliability study for sagittal, frontal, and transversal plane parameters.
Schroeder J; Reer R; Braumann KM
Eur Spine J; 2015 Feb; 24(2):262-9. PubMed ID: 25424686
[TBL] [Abstract][Full Text] [Related]
14. Three-dimensional shape analysis of the scoliotic spine using MR tomography and rasterstereography.
Hierholzer E; Hackenberg L
Stud Health Technol Inform; 2002; 91():184-9. PubMed ID: 15457720
[TBL] [Abstract][Full Text] [Related]
15. Validity and Reliability of Spine Rasterstereography in Patients With Adolescent Idiopathic Scoliosis.
Tabard-Fougère A; Bonnefoy-Mazure A; Hanquinet S; Lascombes P; Armand S; Dayer R
Spine (Phila Pa 1976); 2017 Jan; 42(2):98-105. PubMed ID: 27172281
[TBL] [Abstract][Full Text] [Related]
16. Comparing lesion segmentation methods in multiple sclerosis: Input from one manually delineated subject is sufficient for accurate lesion segmentation.
Weeda MM; Brouwer I; de Vos ML; de Vries MS; Barkhof F; Pouwels PJW; Vrenken H
Neuroimage Clin; 2019; 24():102074. PubMed ID: 31734527
[TBL] [Abstract][Full Text] [Related]
17. Three-dimensional CT texture analysis of anatomic liver segments can differentiate between low-grade and high-grade fibrosis.
Budai BK; Tóth A; Borsos P; Frank VG; Shariati S; Fejér B; Folhoffer A; Szalay F; Bérczi V; Kaposi PN
BMC Med Imaging; 2020 Sep; 20(1):108. PubMed ID: 32957949
[TBL] [Abstract][Full Text] [Related]
18. Rasterstereographic back shape analysis in idiopathic scoliosis after posterior correction and fusion.
Hackenberg L; Hierholzer E; Pötzl W; Götze C; Liljenqvist U
Clin Biomech (Bristol, Avon); 2003 Dec; 18(10):883-9. PubMed ID: 14580831
[TBL] [Abstract][Full Text] [Related]
19. Deep learning prediction of curve severity from rasterstereographic back images in adolescent idiopathic scoliosis.
Minotti M; Negrini S; Cina A; Galbusera F; Zaina F; Bassani T
Eur Spine J; 2023 Dec; ():. PubMed ID: 38055037
[TBL] [Abstract][Full Text] [Related]
20. Lung and Pancreatic Tumor Characterization in the Deep Learning Era: Novel Supervised and Unsupervised Learning Approaches.
Hussein S; Kandel P; Bolan CW; Wallace MB; Bagci U
IEEE Trans Med Imaging; 2019 Aug; 38(8):1777-1787. PubMed ID: 30676950
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
